FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation
This work addresses the scarcity of labeled data in medical imaging for clinical preterm birth risk assessment, providing a standardized benchmark for the domain.
The paper tackled the problem of cervical segmentation in transvaginal ultrasound images for preterm birth risk assessment by introducing the FUGC benchmark, which evaluated semi-supervised learning methods and achieved best results of 90.26% mDSC, 38.88 mHD, and 32.85 ms RT.
Accurate segmentation of cervical structures in transvaginal ultrasound (TVS) is critical for assessing the risk of spontaneous preterm birth (PTB), yet the scarcity of labeled data limits the performance of supervised learning approaches. This paper introduces the Fetal Ultrasound Grand Challenge (FUGC), the first benchmark for semi-supervised learning in cervical segmentation, hosted at ISBI 2025. FUGC provides a dataset of 890 TVS images, including 500 training images, 90 validation images, and 300 test images. Methods were evaluated using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and runtime (RT), with a weighted combination of 0.4/0.4/0.2. The challenge attracted 10 teams with 82 participants submitting innovative solutions. The best-performing methods for each individual metric achieved 90.26\% mDSC, 38.88 mHD, and 32.85 ms RT, respectively. FUGC establishes a standardized benchmark for cervical segmentation, demonstrates the efficacy of semi-supervised methods with limited labeled data, and provides a foundation for AI-assisted clinical PTB risk assessment.